The present invention relates to physiological monitoring and, more particularly, acoustic signal classification in physiological monitoring.
Physiological monitoring is in widespread use managing chronic diseases and in elder care. Physiological monitoring is often performed using wearable devices that acquire and analyze acoustic signals that contain heart and lung sounds as people go about their daily lives. However, such acoustic signals are not always reliable. For example, an acoustic signal may be too noisy to reliably detect heart or lung sounds if taken when a person speaks, or is in motion, or is in an environment with high background noise. Moreover, an acoustic signal may be too weak to reliably detect heart or lung sounds if taken when an acoustic sensor of the monitoring device is not placed at the proper body location or when an air chamber of the acoustic sensor is not fully sealed. When an acoustic signal is too noisy or too weak, confidence in physiological data extracted from the signal, such as the patient's heart or respiration rate, may be very low.
Reliance on physiological data extracted from an unreliable acoustic signal can have serious adverse consequences on patient health. For example, such physiological data can lead a patient or his or her clinician to improperly interpret the patient's physiological state and cause the patient to undergo treatment that is not medically indicated or forego treatment that is medically indicated.
Various methods and systems have been proposed to classify acoustic signals to distinguish between reliable and unreliable signals and “weed out” unreliable signals for purposes of physiological monitoring. However, such classification schemes have generally been non-adaptive and unduly complex.